Simulation of construction operation with direct inputs of physical factors

Abstract: The deterministic approach to estimating the production rate of a
construction operation assumes constant midpoint physical attributes
without addressing the effect of randomness of job conditions. On the other
hand, most simulation models bypass physical factors and rely on secondorder
inputs of probability distributions of task times, the judgements of
which have been cited as difficult for users to make. This paper presents an
alternative approach to production estimation, based on simulating directly
the effects of changing job factors on task times, while addressing the
probabilistic nature of construction. The neural network model is used as
the computing mechanism for determining the cycle times of the equipment
in given conditions and provides the basis for estimation. The obtained
times are then fed directly into a discrete-event simulation model to
simulate the process and establish the production capacity of the system as
constrained by first-order factors. The approach is illustrated using a
hypothetical excavating and hauling operation while the object-oriented
programming technique is used to implement the computing procedure.

Permission to reproduce these papers has been graciously provided by the Research Press of the National Research Council of Canada. The support of the editors, particularly Dr. Dana Vanier, is gratefully appreciated.